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Experimental investigations ANN and GEP modeling of failure load for AA7075-T6/CFRP adhesive bond

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Abstract

It is known that bond strength is affected by the application conditions and methods of adhesive connections. Knowing which method can increase the joint strength more and choosing those methods is important in terms of preventing time loss and financial losses. This study aims to experimentally examine the effects of aluminum alloy and carbon fiber reinforced polymer (CFRP) composite panels on the mechanical properties of adhesive thickness, different temperatures, pressure and filler in adhesive joints and model data. Experiments were carried out by using AA7075-T6/CFRP panels with single lap joint bonding joints, unfilled, 1% and 2% by weight carbon fiber filled, at 19, 50 and 100 °C and also by pressing under 2 MPa pressure without applying pressure. It is understood from the results that the parameters that affect the bond strength the most are the adhesive thickness and the amount of pressing applied on it. The effect of the experiments carried out under these conditions on the tensile failure load values was examined, and also artificial neural network (ANN) and gene expression programming (GEP) models were presented for failure load estimation. The R2 values of the ANN model are 0.9456. The R2 values of the GEP model are 0.9029–0.9572 for training and validation, respectively. High accuracy rates were obtained with both models. It is seen that there is a good agreement between the observed and predicted values in the errors of the training and validation sets. As a result, it was concluded that ANN and GEP models can be used to select the optimum value in similar applications.

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Acknowledgements

The authors appreciate the support for this investigation by Kafkas University. Thank you to Gepsoft limited software company for making GeneXpro program available.

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  1. Department of Mechanical Engineering, Faculty of Engineering and Architecture, Kafkas University, 36200, Kars, Turkey

    Benek Hamamci

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Hamamci, B. Experimental investigations ANN and GEP modeling of failure load for AA7075-T6/CFRP adhesive bond. Neural Comput & Applic 35, 20923–20938 (2023). https://doi.org/10.1007/s00521-023-08796-3

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